1. Multidisciplinary Design Optimisation (MDO)  Different MDO approaches but lack of robust and fast design tools.  Evolutionary/genetic methods perform better with large number of design variables. Example: Coupled problems in aeronautics and aeroelastic wing deformations of smart structures.  What is MDO: Methodology for the design of complex engineering systems in which the strong interaction between the disciplines require the designer to manipulate simultaneously the variables in each of the disciplines involved.

2. Evolution Algorithms What are EAs.  Computers can be adapted to perform this evolution process. Crossover Mutation Fittest Evolution  Based on the Darwinian theory of evolution  Populations of individuals evolve and reproduce by means of mutation and crossover operators and compete in a set environment for survival of the fittest.

3. Multiple Models & Parallel Computing  We use a technique that finds optimum solutions by using many different models, that greatly accelerates the optimisation process. Interactions of the 3 layers: solutions go up and down the layers.  Time-consuming solvers only for the most promising solutions.  Parallel Computing Model 1 precise model Model 2 intermediate model Model 3 approximate model Exploration Exploitation Evolution Algorithm Evaluator

4. Multi-Objective Optimisation and Pareto Front Maximise/ Minimise Subjected to constraints Pareto Optimal Set  Design problems normally require a simultaneous optimisation of conflicting objectives and associated number of constraints. They occur when two or more objectives that cannot be combined rationally.

5. Technical Resources Analysis Tools Aerodynamics/CFD  FLUENT  FLO22 (NASA Langley)  HDASS (In house Navier-Stokes Solver)  (2D Gridfree solver)  VLMpc ( Vortex lattice method)  MSES / XFOIL / NSC2ke CAD  Solid Works, Autocad Aircraft Design  Flight Optimisation System  (FLOPS) NASA Langley  AAA (DART corporation)  ADS (In House) Structural Analysis / FEA  Strand 7, CalculiX

6. Capabilities  We are now confident of our ability to optimise real industrial / Aeronautical cases, which could be three- dimensional, having multi-objective criteria or related to Multidisciplinary Design Optimisation (MDO). - Aerofoil (Inverse Design, Drag Minimization / Gridfree solvers/ SCB) - Wing (Drag and Weight Minimisation) - Whole Aircraft (Drag / weight / noise reduction) - Nozzle (Inverse Design)

7. Aerofoil at Two Different Lifts PropertyFlt. Cond. 1Flt Cond.2 Mach0.75 Reynolds9 x 10 6 Lift0.650.715 Constraints: Thickness > 12.1% x/c (RAE 2822) Max thickness position = 20% ® 55% To solve this and other problems standard industrial flow solvers are being used. Aerofoil c d [c l = 0.65 ] c d [c l = 0.715 ] Traditional Aerofoil RAE2822 0.01470.0185 Conventional Optimiser 0.0098 (-33.3%) 0.0130 (-29.7%) New Technique 0.0094 (-36.1%) 0.0108 (-41.6%)  For a typical 400,000 lb airliner, flying 1,400 hrs/year, a 3% drag reduction corresponds to 580,000 lbs (330,000 L) less fuel burned.

8. SCB (Shock Control Bump) Cd = 0.01986Cd = 0.01808Cd = 0.01622>> Without SCBUpper SCBUpper & Lower SCB Delaying Upper ShockDelaying Upper & Lower Shock Mach Contour

9. M  = 0.8  = 10 o Re = 500 Gridfree Solvers M  = 0.5  = 0 o Re = 5000

10. Features of Gridfree solvers  Gridfree solver require only a cloud of points in the computational domain and connectivity, i.e., a set of neighbors for each point  Gridfree methods don’t care how the cloud of points are generated, i.e., the cloud of points can be obtained from a structures grid or unstructured grid or from a chimera grid.  Generation of good connectivity is critical for the successful application of gridfree solvers, i.e., dense cloud of points is required near the regions of large flow gradients and discontinuities for accurate simulation and good connectivity is required for the solution convergence. Future Research  Development of a random point generator to exploit the true nature of gridfree flow solvers  Coupling Gridfree solvers with Evolutionary Algorithms

11. SCB on 3D Wing Shock Distribution Upper SurfaceLower Surface Without SCB All Section SCB Partial SCB Wing Section Aerofoils Without SCB With All Section SCB With Partial SCB

12. Mach Number0.69 Cruising Altitude10000 ft ClCl 0.19 Wing Area2.94 m 2 Minimisation of wave drag and wing weight MOO of transonic wing design for an Unmanned Aerial Vehicle (UAV)

13. Aerofoil sections for Pareto Member 0 12, 20 Top view of wings on Pareto set Results

14. Aircraft / UAV Design Minimise two objectives  Gross weight  min(WG)  Endurance  min (1/E) Subject to :  Takeoff length < 1000 ft  Alt Cruise > 40000  ROC > 1000 fpm,  Endurance > 24 hrs With respect to:  External geometry of the aircraft Mach = 0.3 Endurance > 24 hrs Cruise Altitude: 40000 ft

15. Pareto Optimal configurations

16. Current and Ongoing Industrial Applications Transonic Viscous Aerodynamic Design Multi-Element High Lift Design Propeller Design AF/A-18 Flutter Model Validation F3 Rear Wing Aerodynamics Problem Two Element Aerofoil Optimisation Problem Transonic Wing Design Aircraft Conceptual Design and Multidisciplinary Optimisation UAV Aerofoil Design 2D Nozzle Inverse Optimisation

17. Conceptual design Preliminary design Detailed Design CAD Integration Approximation Techniques (RSM, Kriging), Optimiser Set (EAS, gradient hybrid) Higher Fidelity Models Database of Case Studies) Parallelization Strategies Multidisciplinary Analysis A Robust Framework for Aeronautical MDO

18.  The results indicate that aircraft design optimisation and shape optimisation problem can be resolved with an evolutionary approach using a hierarchical topology.  The new method contributes to the development of numerical tools required for the complex task of MDO and aircraft design.  No problem specific knowledge is required  The method appears to be broadly applicable to different analysis codes  A family of Pareto optimal configurations was obtained giving the designer a restricted search space to proceed into more details phases of design. Conclusions